Resources

Guides on AI QA & autonomous testing

A small library of in-depth, no-fluff guides on the testing problems we care about most — pre-merge quality gates, testing AI products, self-healing suites, and accessibility.

For engineering leaders evaluating tools11 min read

The best AI testing tools in 2026: an honest comparison

The best AI testing tool depends on your job: testRigor and mabl for plain-English authoring, Applitools for visual AI, QA Wolf for managed coverage, Katalon for all-in-one, and BugBrain for autonomous exploration plus a pre-merge gate.

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For QA & AI-product teams8 min read

Prompt-injection testing: what it is and how to test for it

Prompt-injection testing probes an AI feature with adversarial input designed to override its instructions or leak data — confirming the system holds its guardrails before it ships to users.

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For QA & AI-product teams10 min read

How to test an LLM chatbot: a QA engineer's guide

To test an LLM chatbot, treat it as a non-deterministic feature: check factual accuracy with judges, probe for prompt injection and jailbreaks, validate multi-turn behaviour, and gate on scores instead of exact-match assertions.

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For QA, security & compliance teams9 min read

WCAG 2.2 automated testing & turning test runs into compliance evidence

Automated WCAG 2.2 testing catches the machine-checkable accessibility failures in CI, and the same test runs can produce timestamped, audit-ready evidence for SOC 2, GDPR, and HIPAA — turning QA into a compliance asset.

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For dev teams & engineering leaders9 min read

The pre-merge quality gate: catch bugs before they merge

A PR quality gate is an automated check that tests a pull request before it merges and flags regressions the change introduces — advisory by default, blocking only when it should be.

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For QA engineers & engineering leaders9 min read

Autonomous testing, explained: how AI agents test apps without scripts

Autonomous testing is QA where AI agents decide what to test — exploring an app like a real user, choosing actions, and judging outcomes — instead of replaying a fixed, hand-written script.

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For dev teams & QA engineers7 min read

Test impact analysis: from a diff to the right tests to run

Test impact analysis maps a code change to the application areas it can affect, then runs only the tests that exercise those areas — keeping pull-request feedback fast without sacrificing coverage.

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For QA & test-automation engineers9 min read

Self-healing test automation: how to kill the maintenance tax

Self-healing test automation re-resolves element locators automatically when the UI changes — falling back through a cache, the DOM, the accessibility tree, and vision — so tests stop breaking on cosmetic edits.

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